update app.py

app.py

@@ -2,7 +2,6 @@ import streamlit as st
 from streamlit_chat import message
 from langchain.chains import ConversationalRetrievalChain
 from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.llms import CTransformers
 from langchain.llms import Replicate
 from langchain.text_splitter import CharacterTextSplitter
 from langchain.vectorstores import FAISS
@@ -15,10 +14,8 @@ import os
 from dotenv import load_dotenv
 import tempfile
 
-
 load_dotenv()
 
-
 def initialize_session_state():
     if 'history' not in st.session_state:
         st.session_state['history'] = []
@@ -39,35 +36,31 @@ def display_chat_history(chain):
     container = st.container()
 
     with container:
-        with st.form(key='my_form', clear_on_submit=True):
-            user_input = st.text_input("Question:", placeholder="Ask about your Documents", key='input')
-            submit_button = st.form_submit_button(label='Send')
-
-        if submit_button and user_input:
-            with st.spinner('Generating response...'):
-                output = conversation_chat(user_input, chain, st.session_state['history'])
+        col1, col2 = st.columns(2)
 
-            st.session_state['past'].append(user_input)
-            st.session_state['generated'].append(output)
+        with col1:
+            with st.form(key='my_form', clear_on_submit=True):
+                user_input = st.text_input("Question:", placeholder="Ask about your Documents", key='input')
+                submit_button = st.form_submit_button(label='Send')
 
-    if st.session_state['generated']:
-        with reply_container:
-            for i in range(len(st.session_state['generated'])):
-                message(st.session_state["past"][i], is_user=True, key=str(i) + '_user', avatar_style="thumbs")
-                message(st.session_state["generated"][i], key=str(i), avatar_style="fun-emoji")
+        with col2:
+            if st.session_state['generated']:
+                for i in range(len(st.session_state['generated'])):
+                    message(st.session_state["past"][i], is_user=True, key=str(i) + '_user', avatar_style="thumbs")
+                    message(st.session_state["generated"][i], key=str(i), avatar_style="fun-emoji")
 
 def create_conversational_chain(vector_store):
     load_dotenv()
-    # Create llm
-    #llm = CTransformers(model="llama-2-7b-chat.ggmlv3.q4_0.bin",
-                        #streaming=True,
-                        #callbacks=[StreamingStdOutCallbackHandler()],
-                        #model_type="llama", config={'max_new_tokens': 500, 'temperature': 0.01})
+    replicate_api_token = "r8_AA3K1fhDykqLa5M74E5V0w5ss1z0P9S3foWJl"  # Replace with your actual token
+    os.environ["REPLICATE_API_TOKEN"] = replicate_api_token
+
     llm = Replicate(
-        streaming = True,
-        model = "replicate/llama-2-70b-chat:r8_AA3K1fhDykqLa5M74E5V0w5ss1z0P9S3foWJl",
+        streaming=True,
+        model="replicate/llama-2-70b-chat:58d078176e02c219e11eb4da5a02a7830a283b14cf8f94537af893ccff5ee781",
         callbacks=[StreamingStdOutCallbackHandler()],
-        input = {"temperature": 0.01, "max_length" :500,"top_p":1})
+        input={"temperature": 0.01, "max_length": 500, "top_p": 1},
+        replicate_api_token=replicate_api_token
+    )
     memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
 
     chain = ConversationalRetrievalChain.from_llm(llm=llm, chain_type='stuff',
@@ -78,12 +71,10 @@ def create_conversational_chain(vector_store):
 def main():
     load_dotenv()
     initialize_session_state()
-    st.title("ChatBot ")
-    # Initialize Streamlit
+    st.title("Chat With Your Doc")
     st.sidebar.title("Document Processing")
     uploaded_files = st.sidebar.file_uploader("Upload files", accept_multiple_files=True)
 
-
     if uploaded_files:
         text = []
         for file in uploaded_files:
@@ -107,18 +98,11 @@ def main():
         text_splitter = CharacterTextSplitter(separator="\n", chunk_size=1000, chunk_overlap=100, length_function=len)
         text_chunks = text_splitter.split_documents(text)
 
-        # Create embeddings
         embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2",
                                            model_kwargs={'device': 'cpu'})
-
-        # Create vector store
         vector_store = FAISS.from_documents(text_chunks, embedding=embeddings)
-
-        # Create the chain object
         chain = create_conversational_chain(vector_store)
-
-
         display_chat_history(chain)
 
 if __name__ == "__main__":
-    main()
+    main()